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Deep Learning for Medical Image Processing:Overview,Challenges and the Future - 2018

Deep Learning For Medical Image Processing:Overview,Challenges And The Future

Research Paper on Deep Learning For Medical Image Processing:Overview,Challenges And The Future

Research Area:  Machine Learning


The health care sector is totally different from any other industry. It is a high priority sector and consumers expect the highest level of care and services regardless of cost. The health care sector has not achieved society’s expectations, even though the sector consumes a huge percentage of national budgets. Mostly, the interpretations of medical data are analyzed by medical experts. In terms of a medical expert interpreting images, this is quite limited due to its subjectivity and the complexity of the images; extensive variations exist between experts and fatigue sets in due to their heavy workload. Following the success of deep learning in other real-world applications, it is seen as also providing exciting and accurate solutions for medical imaging, and is seen as a key method for future applications in the health care sector. In this chapter, we discuss state-of-the-art deep learning architecture and its optimization when used for medical image segmentation and classification. The chapter closes with a discussion of the challenges of deep learning methods with regard to medical imaging and open research issue.

Deep Learning
Medical Image Processing
Machine Learning

Author(s) Name:  Muhammad Imran Razzak, Saeeda NazAhmad Zaib

Journal name:  Classification in BioApps

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/978-3-319-65981-7_12

Volume Information: